This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.

```r
library(xgboost)

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Registered S3 method overwritten by ‘data.table’: method from print.data.table




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```r
```r
library(Matrix)
library(mclust)

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__  ___________    __  _____________

/ |/ / ____/ / / / / / / / / /|/ / / / / / / / /_  / /
/ / / / // // // /_/ // /
/
/ //__/_/_//____//_/ version 5.4.9 Type ‘citation()’ for citing this R package in publications.




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```r
```r
library(tidyverse)

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Registered S3 methods overwritten by ‘dbplyr’: method from print.tbl_lazy
print.tbl_sql
─ Attaching packages ─────────────────────────────────── tidyverse 1.3.1 ─ ✓ ggplot2 3.3.5 ✓ purrr 0.3.4 ✓ tibble 3.1.5 ✓ dplyr 1.0.7 ✓ tidyr 1.1.4 ✓ stringr 1.4.0 ✓ readr 2.0.2 ✓ forcats 0.5.1 ─ Conflicts ──────────────────────────────────── tidyverse_conflicts() ─ x tidyr::expand() masks Matrix::expand() x dplyr::filter() masks stats::filter() x dplyr::lag() masks stats::lag() x purrr::map() masks mclust::map() x tidyr::pack() masks Matrix::pack() x dplyr::slice() masks xgboost::slice() x tidyr::unpack() masks Matrix::unpack()




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```r
```r
ds0 <- readRDS(\./ds0.rds\)
ds1 <- readRDS(\./ds1.rds\)

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Loading required package: Seurat Registered S3 method overwritten by ‘htmlwidgets’: method from
print.htmlwidget tools:rstudio Registered S3 method overwritten by ‘spatstat’: method from print.boxx cli




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```r
```r
ds2 <- readRDS(\./ds2.rds\)

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# 分发训练集

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```r
```r
# Idents(ds2) <- ds2$conditions
# ds2_AC <- subset(ds2, idents = \AC\)
# ds2_PA <- subset(ds2, idents = \PA\)
# ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
# ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
# umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
#         scale_x_continuous(limits = c(-5,15),breaks = NULL)
# umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
#         scale_x_continuous(limits = c(-5,15),breaks = NULL)
# 
# AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
# # PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
# write.csv(AC_markers,\AC_SMC_markers.csv\)

ds2_AC <- readRDS(\ds2_AC.rds\)
ds2_PA <- readRDS(\ds2_PA.rds\)

umapplot(ds2_AC)
umapplot(ds2_PA)

ds2_AC$Classification <- Idents(ds2_AC)
Idents(ds2_AC) <- ds2_AC$seurat_clusters
ds2_AC <- RenameIdents(ds2_AC,'0' = '3','1' = '1','2' = '0','3' = '2')
Idents(ds2_AC) <- factor(Idents(ds2_AC),levels = c(0,1,2,3))
ds2_AC$seurat_clusters <- Idents(ds2_AC)

Idents(ds2_PA) <- ds2_PA$seurat_clusters
ds2_PA$Classification <- Idents(ds2_PA)

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## 在AC上预训练

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```r
```r
ds2_AC$Classification <- Idents(ds2_AC)
Idents(ds2_AC) <- ds2_AC$seurat_clusters
AC_data <- get_data_table(ds2_AC, highvar = F, type = \data\)
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],\dgCMatrix\)), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],\dgCMatrix\)), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softmax',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 100

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = \multi:softmax\, eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 100, watchlist, verbose = 0)

eval_loss <- bst_model[[\evaluation_log\]][[\eval_mlogloss\]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = \scatter\, mode = \markers+lines\, 
            marker = list(color = \black\, line = list(color = \#1E90FFC7\, width = 1)),
            line = list(color = \#1E90FF80\, width = 2)) %>% 
  layout(xaxis = list(title = \epoch\),yaxis = list(title = \eval_mlogloss\))

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```r
```r
# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20), n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature, ds2_AC) 
AC_genes <- head(importance, 500) ##选择top500

write.csv(AC_genes, \./datatable/AC_features.csv\, row.names = F)

#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c(\true\,\pre\))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop

confuse_bubblemat(AC_confuse_matrix_test_prop, c(\Fibroblast\, \SMC1\, \Fibromyocyte\, \SMC2\), c(\Fibroblast\, \SMC1\, \Fibromyocyte\, \SMC2\),\AC_pretrain\)

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[[\auc\]] #只需要这个值
adjustedRandIndex(AC_test_data$label, predict_AC_test) #分类器性能

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ARI = 0.9585312


## 在PA上训练

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```r
```r
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)
write.csv(selected_features, \./datatable/selected_features.csv\, row.names = F)

selected_features <- read.csv(\./datatable/selected_features.csv\, stringsAsFactors = F)
selected_features <- selected_features$x
PA_data <- get_data_table(ds2_PA, highvar = F, type = \data\)
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,\dgCMatrix\)), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = \multi:softmax\, eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 100, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()
write.csv(importance, \./datatable/PAtrain_features.csv\, row.names = F)

# multi_featureplot(head(importance,9)$Feature, ds2)

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```r
```r
AC_data <- get_data_table(ds2_AC, highvar = F, type = \data\)
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL
AC_test_data <- list(data = t(as(AC_data,\dgCMatrix\)), label = AC_label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))
AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c(\true\,\pre\))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop  #分析发育轨迹

confuse_bubblemat(AC_confuse_matrix_test_prop,c(\Fibroblast\, \SMC1\, \Fibromyocyte\, \SMC2\), c(\Fibromyocyte\, \SMC1\, \SMC2\), \PAtoAC\)


#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[[\auc\]]

# 计算ARI 
adjustedRandIndex(predict_AC_test, AC_test_data$label)

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ARI = 0.8821278


## 选择特征common genes of top 500
## 使用所有来自PA的细胞训练分类器
## 应用在AC上,计算ARI

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```r
```r
AC_data <- get_data_table(ds2_AC, highvar = F, type = \data\)
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data,\dgCMatrix\)), label = AC_label)

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)

xgb_ACram <- list(eta = 0.2, max_depth = 6,
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = \multi:softmax\, eval_metric = 'mlogloss')

bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 100, verbose = 0)

# 特征提取
importance2 <- xgb.importance(colnames(AC_train), model = bst_model2)
head(importance2)
xgb.ggplot.importance(head(importance2,20),n_clusters = 1) + theme_bw()+theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 8, colour = \black\),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12, colour = \black\),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

write.csv(importance2, \./datatable/ACtrain_features.csv\, row.names = F)
multi_featureplot(head(importance2,9)$Feature, ds2_AC)

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## 应用到AC上

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```r
```r
PA_data <- get_data_table(ds2_PA, highvar = F, type = \data\)
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_test_data <- list(data = t(as(PA_data,\dgCMatrix\)), label = PA_label)

PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

#计算混淆矩阵
predict_PA_test <- round(predict(bst_model2, newdata = PA_test))
 
PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c(\true\,\pre\))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop  #分析发育轨迹

confuse_bubblemat(PA_confuse_matrix_test_prop,c(\Fibromyocyte\, \SMC1\, \SMC2\),c(\Fibroblast\, \SMC1\, \Fibromyocyte\, \SMC2\),session = \ACtoPA\)

# 计算ARI
adjustedRandIndex(predict_PA_test, PA_test_data$label)

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<!-- rnb-text-begin -->

ARI = 0.3024837



# sankey plot
PA -> AC

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```r
```r
Idents(ds2) <- ds2$seurat_clusters
ds2_data <- get_data_table(ds2, highvar = F, type = \data\)
ds2_label <- as.numeric(as.character(Idents(ds2)))

index <- c(1:dim(ds2_data)[2]) %>% sample(ceiling(0.3*dim(ds2_data)[2]), replace = F, prob = NULL)
colnames(ds2_data) <- NULL

ds2_train_data <- list(data = t(as(ds2_data[,-index],\dgCMatrix\)), label = ds2_label[-index])
ds2_test_data <- list(data = t(as(ds2_data[,index],\dgCMatrix\)), label = ds2_label[index])

ds2_train <- xgb.DMatrix(data = ds2_train_data$data,label = ds2_train_data$label)
ds2_test <- xgb.DMatrix(data = ds2_test_data$data,label = ds2_test_data$label)

watchlist <- list(train = ds2_train, eval = ds2_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2))),
                  objective = \multi:softmax\, eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, ds2_train, nrounds = 100, watchlist, verbose = 0)

eval_loss <- bst_model[[\evaluation_log\]][[\eval_mlogloss\]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = \scatter\, mode = \markers+lines\, 
            marker = list(color = \black\, line = list(color = \#1E90FFC7\, width = 1)),
            line = list(color = \#1E90FF80\, width = 2)) %>% 
  layout(xaxis = list(title = \epoch\),yaxis = list(title = \eval_mlogloss\))

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



#把结果投射回umap

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
```r
# 特征提取
importance <- xgb.importance(colnames(ds2_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20), n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature, ds2) 
ds2_genes <- head(importance, 500) ##选择top500

write.csv(ds2_genes, \./datatable/ds2_features.csv\, row.names = F)


predict_ds2_test <- round(predict(bst_model, newdata = ds2_test)) %>% 
#混淆矩阵
ds2_confuse_matrix_test <- table(ds2_test_data$label, predict_ds2_test, dnn=c(\true\,\pre\))
ds2_confuse_matrix_test_prop <- prop.table(ds2_confuse_matrix_test, 1)
ds2_confuse_matrix_test_prop

x <- c(0:4)
y <- c(0:4)
confuse_bubblemat(ds2_confuse_matrix_test_prop,x,y,\ds2_train\)


#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds2_test_data$label, predict_ds2_test) #多分类ROC
xgboost_roc[[\auc\]] #只需要这个值
adjustedRandIndex(ds2_test_data$label, predict_ds2_test) #分类器性能

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


# 反着做
# 选择特征common genes of top 500
## 使用所有来自AC的细胞训练分类器


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
```r
temp <- get_data_table(ds1, highvar = T, type = \data\)
ds1_data <- matrix(data=0, nrow = dim(ds2_data)[1], ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(rownames(ds2_data),colnames(temp)))
for(i in intersect(rownames(ds2_data), rownames(temp))){
  ds1_data[i,] <- temp[i,]
}
rm(temp)
ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL
ds1_test_data <- list(data = t(as(ds1_data,\dgCMatrix\)), label = ds1_label)
ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#预测结果
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

#计算混淆矩阵
ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c(\true\,\pre\))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)

#绘制混淆矩阵
x <- c(\Fibromyocyte\, \SMC1\, \SMC2\)
y <- c(\Fibroblast\, \SMC1\, \Fibromyocyte\, \SMC2\)
confuse_bubblemat(ds1_data_confuse_matrix_test_prop,x,y,\ds2tods1\)

ds1_data_confuse_matrix_test
ds1_data_confuse_matrix_test_prop  #分析发育轨迹
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
xgboost_roc[[\auc\]]

# 计算ARI 
adjustedRandIndex(predict_ds1_test, ds1_test_data$label)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



## 应用在PA上,计算ARI

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuSWRlbnRzKGRzMSkgPC0gcHJlZGljdF9kczFfdGVzdFxuZHMxJHByZWRpY3RfZHMxX3Rlc3QgPC0gcHJlZGljdF9kczFfdGVzdFxudW1hcHBsb3QoZHMxLGdyb3VwLmJ5ID0gXFxwcmVkaWN0X2RzMV90ZXN0XFwpXG5JZGVudHMoZHMxKSA8LSBkczEkc2V1cmF0X2NsdXN0ZXJzXG5gYGBcbmBgYCJ9 -->

```r
```r
Idents(ds1) <- predict_ds1_test
ds1$predict_ds1_test <- predict_ds1_test
umapplot(ds1,group.by = \predict_ds1_test\)
Idents(ds1) <- ds1$seurat_clusters

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

ARI = 0.1797689


## 把结果投射回umap

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
```r
x <- c(\ds0_0\, \ds0_1\, \ds0_2\, \ds0_3\, \ds0_4\)
y <- c(\AC_0\, \AC_1\, \AC_2\)

prop <- as.numeric(ds0_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = \clusters\, y = \inferred from\) + theme_bw()
ggsave(\./plots/ACmodel_humancor.png\, plot = plot, device = png, width = 5,height = 4)

ds0_data_confuse_matrix_test
ds0_data_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC

# 计算ARI 
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

## sankey plot

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
```r
labels <- lapply(levels(Idents(ds2_AC)), paste0, \_AC\) %>% as.character()
labels2 <- lapply(levels(Idents(ds0)), paste0, \_ds0\) %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2))  #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))

plot_ly(type = \sankey\, orientation = \h\,
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = \black\,
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(ds0_data_confuse_matrix_test),
      color = colors
      ))

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



# varify 部分
病变程度量化
# 数据集CA_dataset1
## ds2全体训练


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
Idents(ds2) <- ds2$seurat_clusters
ds2_data <- get_data_table(ds2, highvar = F, type = "data")
ds2_label <- as.numeric(as.character(Idents(ds2)))

index <- c(1:dim(ds2_data)[2]) %>% sample(ceiling(0.3*dim(ds2_data)[2]), replace = F, prob = NULL)
colnames(ds2_data) <- NULL

ds2_train_data <- list(data = t(as(ds2_data[,-index],"dgCMatrix")), label = ds2_label[-index])
ds2_test_data <- list(data = t(as(ds2_data[,index],"dgCMatrix")), label = ds2_label[index])

ds2_train <- xgb.DMatrix(data = ds2_train_data$data,label = ds2_train_data$label)
ds2_test <- xgb.DMatrix(data = ds2_test_data$data,label = ds2_test_data$label)

watchlist <- list(train = ds2_train, eval = ds2_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, ds2_train, nrounds = 100, watchlist, verbose = 0)

eval_loss <- bst_model[["evaluation_log"]][["eval_mlogloss"]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = "scatter", mode = "markers+lines", 
            marker = list(color = "black", line = list(color = "#1E90FFC7", width = 1)),
            line = list(color = "#1E90FF80", width = 2)) %>% 
  layout(xaxis = list(title = "epoch"),yaxis = list(title = "eval_mlogloss"))
adjustedRandIndex(ds2_test_data$label, predict_ds2_test) #分类器性能
[1] 0.8831684
```r
lym_PA_data <- get_data_table(lym_ds2_PA, highvar = F, type = \data\)
lym_PA_label <- as.numeric(as.character(Idents(lym_ds2_PA)))

set.seed(7)
index <- c(1:dim(lym_PA_data)[2]) %>% sample(ceiling(0.3*dim(lym_PA_data)[2]), replace = F, prob = NULL)
colnames(lym_PA_data) <- NULL
lym_PA_train_data <- list(data = t(as(lym_PA_data[,-index],\dgCMatrix\)), label = lym_PA_label[-index])
lym_PA_test_data <- list(data = t(as(lym_PA_data[,index],\dgCMatrix\)), label = lym_PA_label[index])

lym_PA_train <- xgb.DMatrix(data = lym_PA_train_data$data,label = lym_PA_train_data$label)
lym_PA_test <- xgb.DMatrix(data = lym_PA_test_data$data,label = lym_PA_test_data$label)

watchlist <- list(train = lym_PA_train, eval = lym_PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(lym_ds2_PA))),
                  objective = \multi:softmax\, eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, lym_PA_train, nrounds = 100, watchlist, verbose = 0)

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ARI = 0.2321442


## 投射回umap

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<!-- rnb-source-begin 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 -->

```r
```r
labels <- lapply(levels(Idents(lym_ds2_PA)), paste0, \_lymPA\) %>% as.character()
labels2 <- lapply(levels(Idents(lym_ds2_AC)), paste0, \_lymAC\) %>% as.character()
sources <- rep(0:5, each = 5)  #注意这里的each和times的区别
colors <- rep(colors_list[1:6], each = 5)
targets <- rep(6:10, times = 6)

plot_ly(type = \sankey\, orientation = \h\,
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = \black\,
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(lym_AC_confuse_matrix_test),
      color = colors
      ))


umapplot(lym_ds2_AC)
umapplot(lym_ds2_PA)

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# 冠状动脉数据集

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<!-- rnb-source-begin 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 -->

```r
```r
sankey_plot <- function(confuse_matrix, label1, label2, session = \session\)
{
  sources <- rep(0:(length(label1)-1), each = length(label2))  #注意这里的each和times的区别
  colors <- rep(aero_colors_list[1:length(label1)], each = length(label2))
  targets <- rep(length(label1)+0:(length(label2)-1), times = length(label1))

  plot_ly(type = \sankey\, orientation = \h\,
      node = list(
        label = c(label1,label2), 
        color = colors_list, pad = 15, thickness = 30,
        line = list(color = \black\, width = 1)),
      link = list(
        source = sources, target = targets,
        value =  as.numeric(confuse_matrix),
        color = colors
        )) %>% layout(title=session, font=list(family = \Arial\,size = 20, color = 'black'))
}


confuse_bubblemat <- function(confuse_matrix_prop, label1, label2, session = \session\)
{
prop <- as.numeric(confuse_matrix_prop)
data <- expand.grid(x = label1, y = label2) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = \clusters\, y = \inferred from\) + theme_bw()

ggsave(paste0(session, \.svg\), plot = plot, device = svg, width = 5,height = 4)
}

## 返回最大的概率对应的index
func <- function(s, ident)
{
  if(max(s)>1.2/length(ident))
    return(ident[which(s == max(s))])
  else
    return(\unassigned\)
}

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```r
```r
aero_colors_list <- as.character(lapply(colors_list, paste0, \A0\)) #透明化颜色

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<!-- rnb-source-begin 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 -->

```r
x <- c("ds0_0", "ds0_1", "ds0_2", "ds0_3", "ds0_4")
y <- c("AC_0", "AC_1", "AC_2")

prop <- as.numeric(ds0_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/ACmodel_humancor.png", plot = plot, device = png, width = 5,height = 4)

ds0_data_confuse_matrix_test
ds0_data_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC

# 计算ARI 
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
ARI = 0.7047121
labels <- lapply(levels(Idents(ds2_AC)), paste0, "_AC") %>% as.character()
labels2 <- lapply(levels(Idents(ds0)), paste0, "_ds0") %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2))  #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(ds0_data_confuse_matrix_test),
      color = colors
      ))
# load("./init.RData")
multi_featureplot(head(importance2,9)$Feature, ds2_AC)
multi_featureplot(head(importance2,9)$Feature, ds0)
multi_featureplot(head(importance2,9)$Feature, ds1)
f("MYH11", ds2_AC)
umapplot(ds0)

淋巴细胞

# lym_ds2 <- subset(CA_dataset2, idents = c('0','4','9'))
lym_ds2 <- readRDS("lym_ds2.rds")
Idents(lym_ds2) <- lym_ds2$conditions
lym_ds2_AC <- subset(lym_ds2, idents = "AC")
lym_ds2_PA <- subset(lym_ds2, idents = "PA")
lym_ds2_AC <- lym_ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(lym_ds2_AC)
lym_ds2_PA <- lym_ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(lym_ds2_PA)

# ggsave("./supp/lym_ds2_PA.svg", plot = umapplot(lym_ds2_PA), device = svg, width = 7, height = 6)
# ggsave("./supp/lym_ds2_AC.svg", plot = umapplot(lym_ds2_AC), device = svg, width = 7, height = 6)

用PA的lym训练

lym_PA_data <- get_data_table(lym_ds2_PA, highvar = F, type = "data")
lym_PA_label <- as.numeric(as.character(Idents(lym_ds2_PA)))

set.seed(7)
index <- c(1:dim(lym_PA_data)[2]) %>% sample(ceiling(0.3*dim(lym_PA_data)[2]), replace = F, prob = NULL)
colnames(lym_PA_data) <- NULL
lym_PA_train_data <- list(data = t(as(lym_PA_data[,-index],"dgCMatrix")), label = lym_PA_label[-index])
lym_PA_test_data <- list(data = t(as(lym_PA_data[,index],"dgCMatrix")), label = lym_PA_label[index])

lym_PA_train <- xgb.DMatrix(data = lym_PA_train_data$data,label = lym_PA_train_data$label)
lym_PA_test <- xgb.DMatrix(data = lym_PA_test_data$data,label = lym_PA_test_data$label)

watchlist <- list(train = lym_PA_train, eval = lym_PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(lym_ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, lym_PA_train, nrounds = 100, watchlist, verbose = 0)
# 特征提取
importance <- xgb.importance(colnames(lym_PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()+theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 8, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

lym_PA_genes <- head(importance, 500) ##选择top500
multi_featureplot(lym_PA_genes$Feature[1:9],lym_ds2_PA,labels = "")
write.csv(lym_PA_genes,"./datatable/lym_PA_features.csv", row.names = F)
#混淆矩阵
predict_lym_PA_test <- round(predict(bst_model, newdata = lym_PA_test))

lym_PA_confuse_matrix_test <- table(lym_PA_test_data$label, predict_lym_PA_test, dnn=c("true","pre"))
lym_PA_confuse_matrix_test_prop <- prop.table(lym_PA_confuse_matrix_test, 1)
lym_PA_confuse_matrix_test_prop

x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")

prop <- as.numeric(lym_PA_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel.png", plot = plot, device = png, width = 7,height = 6)

用AC的lym验证

lym_AC_data <- get_data_table(lym_ds2_AC, highvar = F, type = "data")
lym_AC_label <- as.numeric(as.character(Idents(lym_ds2_AC)))
colnames(lym_AC_data) <- NULL
lym_AC_test_data <- list(data = t(as(lym_AC_data,"dgCMatrix")), label = lym_AC_label)
lym_AC_test <- xgb.DMatrix(data = lym_AC_test_data$data,label = lym_AC_test_data$label)

predict_lym_AC_test <- round(predict(bst_model, newdata = lym_AC_test))

lym_AC_confuse_matrix_test <- table(lym_AC_test_data$label, predict_lym_AC_test, dnn=c("true","pre"))
lym_AC_confuse_matrix_test_prop <- prop.table(lym_AC_confuse_matrix_test, 1)
lym_AC_confuse_matrix_test_prop


x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4")

prop <- as.numeric(lym_AC_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel_AC.png", plot = plot, device = png, width = 7,height = 6)

xgboost_roc[["auc"]]
adjustedRandIndex(predict_lym_AC_test, lym_AC_test_data$label)
lym_AC_confuse_matrix_test_prop

sankey_plot(lym_AC_confuse_matrix_test,session = "PAtoAC_lym")
ARI = 0.7213791
labels <- lapply(levels(Idents(lym_ds2_PA)), paste0, "_lymPA") %>% as.character()
labels2 <- lapply(levels(Idents(lym_ds2_AC)), paste0, "_lymAC") %>% as.character()
sources <- rep(0:5, each = 5)  #注意这里的each和times的区别
colors <- rep(colors_list[1:6], each = 5)
targets <- rep(6:10, times = 6)

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(lym_AC_confuse_matrix_test),
      color = colors
      ))


umapplot(lym_ds2_AC)
umapplot(lym_ds2_PA)

functions set

sankey_plot <- function(confuse_matrix, label1, label2, session = "session")
{
  sources <- rep(0:(length(label1)-1), each = length(label2))  #注意这里的each和times的区别
  colors <- rep(aero_colors_list[1:length(label1)], each = length(label2))
  targets <- rep(length(label1)+0:(length(label2)-1), times = length(label1))

  plot_ly(type = "sankey", orientation = "h",
      node = list(
        label = c(label1,label2), 
        color = colors_list, pad = 15, thickness = 30,
        line = list(color = "black", width = 1)),
      link = list(
        source = sources, target = targets,
        value =  as.numeric(confuse_matrix),
        color = colors
        )) %>% layout(title=session, font=list(family = "Arial",size = 20, color = 'black'))
}


confuse_bubblemat <- function(confuse_matrix_prop, label1, label2, session = "session")
{
prop <- as.numeric(confuse_matrix_prop)
data <- expand.grid(x = label1, y = label2) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()

ggsave(paste0(session, ".svg"), plot = plot, device = svg, width = 5,height = 4)
}

## 返回最大的概率对应的index
func <- function(s, ident)
{
  if(max(s)>1.2/length(ident))
    return(ident[which(s == max(s))])
  else
    return("unassigned")
}

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(xgboost)
library(Matrix)
library(mclust)
library(tidyverse)
```

```{r}
ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")
```

# 分发训练集
```{r}
# Idents(ds2) <- ds2$conditions
# ds2_AC <- subset(ds2, idents = "AC")
# ds2_PA <- subset(ds2, idents = "PA")
# ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
# ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
# umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
#         scale_x_continuous(limits = c(-5,15),breaks = NULL)
# umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
#         scale_x_continuous(limits = c(-5,15),breaks = NULL)
# 
# AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
# # PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
# write.csv(AC_markers,"AC_SMC_markers.csv")

ds2_AC <- readRDS("ds2_AC.rds")
ds2_PA <- readRDS("ds2_PA.rds")

umapplot(ds2_AC)
umapplot(ds2_PA)

ds2_AC$Classification <- Idents(ds2_AC)
Idents(ds2_AC) <- ds2_AC$seurat_clusters
ds2_AC <- RenameIdents(ds2_AC,'0' = '3','1' = '1','2' = '0','3' = '2')
Idents(ds2_AC) <- factor(Idents(ds2_AC),levels = c(0,1,2,3))
ds2_AC$seurat_clusters <- Idents(ds2_AC)

Idents(ds2_PA) <- ds2_PA$seurat_clusters
ds2_PA$Classification <- Idents(ds2_PA)


```




## 在AC上预训练
```{r}
ds2_AC$Classification <- Idents(ds2_AC)
Idents(ds2_AC) <- ds2_AC$seurat_clusters
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softmax',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 100

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 100, watchlist, verbose = 0)

eval_loss <- bst_model[["evaluation_log"]][["eval_mlogloss"]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = "scatter", mode = "markers+lines", 
            marker = list(color = "black", line = list(color = "#1E90FFC7", width = 1)),
            line = list(color = "#1E90FF80", width = 2)) %>% 
  layout(xaxis = list(title = "epoch"),yaxis = list(title = "eval_mlogloss"))

```

```{r,fig.height=4,fig.width=4}
# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20), n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature, ds2_AC) 
AC_genes <- head(importance, 500) ##选择top500

write.csv(AC_genes, "./datatable/AC_features.csv", row.names = F)

#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop

confuse_bubblemat(AC_confuse_matrix_test_prop, c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2"), c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2"),"AC_pretrain")

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[["auc"]] #只需要这个值
adjustedRandIndex(AC_test_data$label, predict_AC_test) #分类器性能
```
```
ARI = 0.9585312
```

## 在PA上训练
```{r}
ds2_PA$Classification <- Idents(ds2_PA)
Idents(ds2_PA) <- ds2_PA$seurat_clusters

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 100, watchlist, verbose = 0)
eval_loss <- bst_model[["evaluation_log"]][["eval_mlogloss"]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = "scatter", mode = "markers+lines", 
            marker = list(color = "black", line = list(color = "#1E90FFC7", width = 1)),
            line = list(color = "#1E90FF80", width = 2)) %>% 
  layout(xaxis = list(title = "epoch"),yaxis = list(title = "eval_mlogloss"))
```

```{r,fig.height=4,fig.width=4}
# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature, ds2_PA)
PA_genes <- head(importance, 500) ##选择top500
write.csv(PA_genes, "./datatable/PA_features.csv", row.names = F)

#混淆矩阵
predict_PA_test <- round(predict(bst_model, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop

confuse_bubblemat(PA_confuse_matrix_test_prop,c("Fibromyocyte", "SMC1", "SMC2"),c("Fibromyocyte", "SMC1", "SMC2"),"PA_pretrain")

#ROC曲线

xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc[["auc"]]
adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能
```
```
ARI = 0.8821278
```

## 选择特征common genes of top 500
## 使用所有来自PA的细胞训练分类器
## 应用在AC上，计算ARI
```{r,fig.height=4,fig.width=4}
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)
write.csv(selected_features, "./datatable/selected_features.csv", row.names = F)

selected_features <- read.csv("./datatable/selected_features.csv", stringsAsFactors = F)
selected_features <- selected_features$x
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 100, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()
write.csv(importance, "./datatable/PAtrain_features.csv", row.names = F)

# multi_featureplot(head(importance,9)$Feature, ds2)
```

## 应用到AC上
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL
AC_test_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))
AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop  #分析发育轨迹

confuse_bubblemat(AC_confuse_matrix_test_prop,c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2"), c("Fibromyocyte", "SMC1", "SMC2"), "PAtoAC")


#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[["auc"]]

# 计算ARI 
adjustedRandIndex(predict_AC_test, AC_test_data$label)
```
```
ARI = 0.3024837
```


# sankey plot
PA -> AC
```{r fig.width=6,fig.height=4}
sankey_plot(AC_confuse_matrix_test, label1 = c("Fibroblast", "SMC1", "SMC2"), label2 = c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2"), session = "PA -> AC")

umapplot(ds2_AC)
umapplot(ds2_PA)
# umapplot(ds2,split.by = "conditions")
```


#把结果投射回umap
```{r}
Idents(ds2_AC) <- predict_AC_test
ds2_AC$predict_AC_test <- predict_AC_test
umapplot(ds2_AC,group.by = "predict_AC_test")
Idents(ds2_AC) <- ds2_AC$seurat_clusters
```

# 反着做
# 选择特征common genes of top 500
## 使用所有来自AC的细胞训练分类器

```{r,fig.height=6,fig.width=6}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)

xgb_ACram <- list(eta = 0.2, max_depth = 6,
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 100, verbose = 0)

# 特征提取
importance2 <- xgb.importance(colnames(AC_train), model = bst_model2)
head(importance2)
xgb.ggplot.importance(head(importance2,20),n_clusters = 1) + theme_bw()+theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 8, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

write.csv(importance2, "./datatable/ACtrain_features.csv", row.names = F)
multi_featureplot(head(importance2,9)$Feature, ds2_AC)

```


## 应用在PA上，计算ARI
```{r}
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_test_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

#计算混淆矩阵
predict_PA_test <- round(predict(bst_model2, newdata = PA_test))
 
PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop  #分析发育轨迹

confuse_bubblemat(PA_confuse_matrix_test_prop,c("Fibromyocyte", "SMC1", "SMC2"),c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2"),session = "ACtoPA")

# 计算ARI
adjustedRandIndex(predict_PA_test, PA_test_data$label)
```
```
ARI = 0.1797689
```

## 把结果投射回umap
```{r}
Idents(ds2_PA) <- predict_PA_test
ds2_PA$predict_PA_test <- predict_PA_test
umapplot(ds2_PA,group.by = "predict_PA_test")
Idents(ds2_PA) <- ds2_PA$seurat_clusters
```
## sankey plot
```{r}
labels <- c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2")
labels2 <- c("Fibromyocyte", "SMC1", "SMC2")
sankey_plot(PA_confuse_matrix_test,labels,labels2,session = "AC -> PA")
```


# varify 部分
病变程度量化
# 数据集CA_dataset1
## ds2全体训练

```{r}
Idents(ds2) <- ds2$seurat_clusters
ds2_data <- get_data_table(ds2, highvar = F, type = "data")
ds2_label <- as.numeric(as.character(Idents(ds2)))

index <- c(1:dim(ds2_data)[2]) %>% sample(ceiling(0.3*dim(ds2_data)[2]), replace = F, prob = NULL)
colnames(ds2_data) <- NULL

ds2_train_data <- list(data = t(as(ds2_data[,-index],"dgCMatrix")), label = ds2_label[-index])
ds2_test_data <- list(data = t(as(ds2_data[,index],"dgCMatrix")), label = ds2_label[index])

ds2_train <- xgb.DMatrix(data = ds2_train_data$data,label = ds2_train_data$label)
ds2_test <- xgb.DMatrix(data = ds2_test_data$data,label = ds2_test_data$label)

watchlist <- list(train = ds2_train, eval = ds2_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, ds2_train, nrounds = 100, watchlist, verbose = 0)

eval_loss <- bst_model[["evaluation_log"]][["eval_mlogloss"]]
plot_ly(data.frame(eval_loss), x = c(1:100), y = eval_loss) %>% 
  add_trace(type = "scatter", mode = "markers+lines", 
            marker = list(color = "black", line = list(color = "#1E90FFC7", width = 1)),
            line = list(color = "#1E90FF80", width = 2)) %>% 
  layout(xaxis = list(title = "epoch"),yaxis = list(title = "eval_mlogloss"))
```

```{r,fig.height=6,fig.width=6}
# 特征提取
importance <- xgb.importance(colnames(ds2_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()+theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 8, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

multi_featureplot(head(importance,9)$Feature, ds2) 
ds2_genes <- head(importance, 500) ##选择top500

write.csv(ds2_genes, "./datatable/ds2_features.csv", row.names = F)


predict_ds2_test <- round(predict(bst_model, newdata = ds2_test))
#混淆矩阵
ds2_confuse_matrix_test <- table(ds2_test_data$label, predict_ds2_test, dnn=c("true","pre"))
ds2_confuse_matrix_test_prop <- prop.table(ds2_confuse_matrix_test, 1)
ds2_confuse_matrix_test_prop

x <- c(0:4)
y <- c(0:4)
confuse_bubblemat(ds2_confuse_matrix_test_prop,x,y,"ds2_train")


#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds2_test_data$label, predict_ds2_test) #多分类ROC
xgboost_roc[["auc"]] #只需要这个值
adjustedRandIndex(ds2_test_data$label, predict_ds2_test) #分类器性能
```


```{r}
temp <- get_data_table(ds1, highvar = T, type = "data")
ds1_data <- matrix(data=0, nrow = dim(ds2_data)[1], ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(rownames(ds2_data),colnames(temp)))
for(i in intersect(rownames(ds2_data), rownames(temp))){
  ds1_data[i,] <- temp[i,]
}
rm(temp)
ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL
ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)
ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#预测结果
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

#计算混淆矩阵
ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)

#绘制混淆矩阵
x <- c("Fibromyocyte", "SMC1", "SMC2")
y <- c("Fibroblast", "SMC1", "Fibromyocyte", "SMC2")
confuse_bubblemat(ds1_data_confuse_matrix_test_prop,x,y,"ds2tods1")

ds1_data_confuse_matrix_test
ds1_data_confuse_matrix_test_prop  #分析发育轨迹
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
xgboost_roc[["auc"]]

# 计算ARI 
adjustedRandIndex(predict_ds1_test, ds1_test_data$label)
```
```
ARI = 0.2321442
```

## 投射回umap
```{r}
Idents(ds1) <- predict_ds1_test
ds1$predict_ds1_test <- predict_ds1_test
umapplot(ds1,group.by = "predict_ds1_test")
Idents(ds1) <- ds1$seurat_clusters
```


# 冠状动脉数据集
```{r}
ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds0)
f("TAGLN",ds0)
# ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.7, min.diff.pct = 0.2)
```

```{r}
selected_features <- AC_genes$Feature
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features), 
                   ncol = length(colnames(temp)), byrow = FALSE, 
                   dimnames = list(selected_features,colnames(temp)))
for(i in intersect(selected_features,rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
rm(temp)

ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))

ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
```


```{r}
x <- c("ds0_0", "ds0_1", "ds0_2", "ds0_3", "ds0_4")
y <- c("AC_0", "AC_1", "AC_2")

prop <- as.numeric(ds0_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/ACmodel_humancor.png", plot = plot, device = png, width = 5,height = 4)

ds0_data_confuse_matrix_test
ds0_data_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC

# 计算ARI 
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
```
```
ARI = 0.7047121
```

```{r}
labels <- lapply(levels(Idents(ds2_AC)), paste0, "_AC") %>% as.character()
labels2 <- lapply(levels(Idents(ds0)), paste0, "_ds0") %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2))  #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(ds0_data_confuse_matrix_test),
      color = colors
      ))
```


```{r}
# load("./init.RData")
multi_featureplot(head(importance2,9)$Feature, ds2_AC)
multi_featureplot(head(importance2,9)$Feature, ds0)
multi_featureplot(head(importance2,9)$Feature, ds1)
f("MYH11", ds2_AC)
umapplot(ds0)
```

# 淋巴细胞

```{r}
# lym_ds2 <- subset(CA_dataset2, idents = c('0','4','9'))
lym_ds2 <- readRDS("lym_ds2.rds")
Idents(lym_ds2) <- lym_ds2$conditions
lym_ds2_AC <- subset(lym_ds2, idents = "AC")
lym_ds2_PA <- subset(lym_ds2, idents = "PA")
lym_ds2_AC <- lym_ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(lym_ds2_AC)
lym_ds2_PA <- lym_ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(lym_ds2_PA)

# ggsave("./supp/lym_ds2_PA.svg", plot = umapplot(lym_ds2_PA), device = svg, width = 7, height = 6)
# ggsave("./supp/lym_ds2_AC.svg", plot = umapplot(lym_ds2_AC), device = svg, width = 7, height = 6)
```

## 用PA的lym训练
```{r}
lym_PA_data <- get_data_table(lym_ds2_PA, highvar = F, type = "data")
lym_PA_label <- as.numeric(as.character(Idents(lym_ds2_PA)))

set.seed(7)
index <- c(1:dim(lym_PA_data)[2]) %>% sample(ceiling(0.3*dim(lym_PA_data)[2]), replace = F, prob = NULL)
colnames(lym_PA_data) <- NULL
lym_PA_train_data <- list(data = t(as(lym_PA_data[,-index],"dgCMatrix")), label = lym_PA_label[-index])
lym_PA_test_data <- list(data = t(as(lym_PA_data[,index],"dgCMatrix")), label = lym_PA_label[index])

lym_PA_train <- xgb.DMatrix(data = lym_PA_train_data$data,label = lym_PA_train_data$label)
lym_PA_test <- xgb.DMatrix(data = lym_PA_test_data$data,label = lym_PA_test_data$label)

watchlist <- list(train = lym_PA_train, eval = lym_PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(lym_ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, lym_PA_train, nrounds = 100, watchlist, verbose = 0)
```


```{r fig.height=6,fig.width=6}
# 特征提取
importance <- xgb.importance(colnames(lym_PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()+theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 8, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

lym_PA_genes <- head(importance, 500) ##选择top500
multi_featureplot(lym_PA_genes$Feature[1:9],lym_ds2_PA,labels = "")
write.csv(lym_PA_genes,"./datatable/lym_PA_features.csv", row.names = F)
#混淆矩阵
predict_lym_PA_test <- round(predict(bst_model, newdata = lym_PA_test))

lym_PA_confuse_matrix_test <- table(lym_PA_test_data$label, predict_lym_PA_test, dnn=c("true","pre"))
lym_PA_confuse_matrix_test_prop <- prop.table(lym_PA_confuse_matrix_test, 1)
lym_PA_confuse_matrix_test_prop

x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")

prop <- as.numeric(lym_PA_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel.png", plot = plot, device = png, width = 7,height = 6)
```


## 用AC的lym验证
```{r}
lym_AC_data <- get_data_table(lym_ds2_AC, highvar = F, type = "data")
lym_AC_label <- as.numeric(as.character(Idents(lym_ds2_AC)))
colnames(lym_AC_data) <- NULL
lym_AC_test_data <- list(data = t(as(lym_AC_data,"dgCMatrix")), label = lym_AC_label)
lym_AC_test <- xgb.DMatrix(data = lym_AC_test_data$data,label = lym_AC_test_data$label)

predict_lym_AC_test <- round(predict(bst_model, newdata = lym_AC_test))

lym_AC_confuse_matrix_test <- table(lym_AC_test_data$label, predict_lym_AC_test, dnn=c("true","pre"))
lym_AC_confuse_matrix_test_prop <- prop.table(lym_AC_confuse_matrix_test, 1)
lym_AC_confuse_matrix_test_prop


x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4")

prop <- as.numeric(lym_AC_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel_AC.png", plot = plot, device = png, width = 7,height = 6)

xgboost_roc[["auc"]]
adjustedRandIndex(predict_lym_AC_test, lym_AC_test_data$label)
lym_AC_confuse_matrix_test_prop

sankey_plot(lym_AC_confuse_matrix_test,session = "PAtoAC_lym")
```
``` 
ARI = 0.7213791
```

```{r}
labels <- lapply(levels(Idents(lym_ds2_PA)), paste0, "_lymPA") %>% as.character()
labels2 <- lapply(levels(Idents(lym_ds2_AC)), paste0, "_lymAC") %>% as.character()
sources <- rep(0:5, each = 5)  #注意这里的each和times的区别
colors <- rep(colors_list[1:6], each = 5)
targets <- rep(6:10, times = 6)

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(lym_AC_confuse_matrix_test),
      color = colors
      ))


umapplot(lym_ds2_AC)
umapplot(lym_ds2_PA)
```


## functions set
```{r}
sankey_plot <- function(confuse_matrix, label1, label2, session = "session")
{
  sources <- rep(0:(length(label1)-1), each = length(label2))  #注意这里的each和times的区别
  colors <- rep(aero_colors_list[1:length(label1)], each = length(label2))
  targets <- rep(length(label1)+0:(length(label2)-1), times = length(label1))

  plot_ly(type = "sankey", orientation = "h",
      node = list(
        label = c(label1,label2), 
        color = colors_list, pad = 15, thickness = 30,
        line = list(color = "black", width = 1)),
      link = list(
        source = sources, target = targets,
        value =  as.numeric(confuse_matrix),
        color = colors
        )) %>% layout(title=session, font=list(family = "Arial",size = 20, color = 'black'))
}


confuse_bubblemat <- function(confuse_matrix_prop, label1, label2, session = "session")
{
prop <- as.numeric(confuse_matrix_prop)
data <- expand.grid(x = label1, y = label2) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()

ggsave(paste0(session, ".svg"), plot = plot, device = svg, width = 5,height = 4)
}

## 返回最大的概率对应的index
func <- function(s, ident)
{
  if(max(s)>1.2/length(ident))
    return(ident[which(s == max(s))])
  else
    return("unassigned")
}

```



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